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Completing the Real-Time Traffic Picture Completing the Real-Time Traffic Picture

Completing the Real-Time Traffic Picture - PowerPoint Presentation

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Completing the Real-Time Traffic Picture - PPT Presentation

Stanley E Young PE PhD University of Maryland Center for Advanced Transportation Technology Traffax Inc Outline Vehicle Probe Project Where we have been where we are now Completing the Picture ID: 749771

probe data travel time data probe time travel performance arterial vehicle signal 2014 based system 2015 management real systems

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Slide1

Completing the Real-Time Traffic Picture

Stanley E. Young, P.E. Ph.D.

University of Maryland

Center for Advanced Transportation Technology

Traffax Inc.Slide2

Outline

Vehicle Probe Project

Where we have been, where we are now ….

Completing the Picture

Bringing in Volume Data – State Wide

Extending Real-Time to Arterial Networks

Its time for Arterial Management Systems …Slide3

I-95 Corridor Coalition

Maine to Florida

Road, Rail, and Water Transport

DOTs, Toll authorities, Public Safety, and other related organizations

Forum for management and operations issues

Volunteer organization

Goal: Improve Transportation System PerformanceMulti-jurisdictional cooperation, >20 yearsSlide4

Vehicle Probe Project

Phase I (2008-2014)

First Probe-based Traffic System

Specifications-based, validated

Licensing - one buys, all share

Began 2.5K miles, grew to 40K

Travel time on signs, 511 systems, operational awareness, performance measuresPhase II (2014 forward)All of the aboveBetter quality, less costData market place (Multiple-vendors)Emphasis on arterials and latency42.5K and growingMap-21 Performance MeasuresSlide5

Vehicle Probe Project

Phase I (2008-2014)

First Probe-based Traffic System

Specifications-based, validated

Licensing - one buys, all share

Began 2.5K miles, grew to 40K

Travel time on signs, 511 systems, operational awareness, performance measuresPhase II (2014 forward)All of the aboveBetter quality, less costData market place (Multiple-vendors)Emphasis on arterials and latency42.5K and growingMap-21 Performance MeasuresSlide6

First Multi-Vendor Freeway Validation

I-83

& I-81

Harrisburg, Oct 2014

PA-08

14 Segments

31.3 milesData collection2300 to 2555 total hrs71 to 80 hrs [0-30]53 to 66 hrs [30-45]AASE2.1 to 4.1 mph [0-30]3.1 to 5.8 mph [30-45]

6

May 07, 2015Slide7

PM Peak Hour (Oct 15-16, 2014)

May 07, 2015

7Slide8

May 07, 2015

8

Non-recurring Congestion

Oct 13, 2014 10 AM to 7 PMSlide9

Integrating Real-Time Volume

Objective: Provide volume data in real-time on freeways and high-level

a

rterials in a method similar to probe based speed and travel time data

Approach

Cooperative Research Initiative with IndustryCalibration/validation

test bedFocus group to refine productVendors develop, test, and reportGoal is to accelerate timeframe to viable real-time volume data feedIf interested contact rmjcar@umd.edu Slide10

Arterial Probe Data Quality Study

2013 – mid 2014

10

State / Set ID

Road Number

Road Name

Validation Date Span

# of Segments

# of Through Lanes

AADT Range (in 1000s)

Length*

(mile)

# Signals / Density

# of Access Points

Median Barrier

Speed Limit

(mph)

NJ-11

US-1

Trenton Fwy, Brunswick Pike

Sep 10 - 24, 2013

10

2-4

33 - 90

14.2

10 / 0.7

112

Yes

55

NJ-42

Black Horse Pike

8

2

25-5412.523 / 1.8260Yes45-50US-130Burlington Pike1034214.328 / 2.0229Yes50NJ-12NJ-38Kaighn Ave.Nov 5-19, 2013162-432-8024.544 / 1.8235Yes50NJ-73Palmyra Bridge Rd.182-433-7423.941 / 1.7236Yes45-55PA-05US-1Lincoln HighwayDec 3 - 14, 2013282 - 3+321 - 10030.62107 / 3.5178Yes40 - 50US-322Conchester Highway61-222 - 3414.287 / 0.548No35 - 45PA-06PA-611Easton RdJan 9 - 22, 2014102-418-316.721/ 3.1398NO40-45PA-611Old York Rd81-221-307.326/ 3.56105Partial15-40PA-611N Broad St162-417-329.6102/ 10.62161NO15-40VA-07VA-7Leesburg Pike and Harry Byrd HwyApril 5-16, 2014302-445-6030.557 / 1.9203Yes35-55US-29Lee Hwy (S Washington St) 4214-254.422 / 5114Partial30VA-08US-29Lee HwyMay 8-19, 2014262-415-4531.9115/3.6287Partial35-50MD-08MD-140Reistertown RdJune 5-14, 2014121 - 319-4410.540 / 3.8148No30-40Baltimore Blvd62 - 440-5311.016/ 1.538YES45-55

9 Case Studies from 2013-14Spans NJ through NCTest extent of probe data15K AADT to 100K2 – 12 lanes0.5 to 10+ signals per mileObjective: Reference case studies

April 30, 2015Slide11

Arterial Probe Data

Recommendations

11

Probe data quality most correlated to signal density

Increased volume aids probe data quality, but does not overcome issues resulting from high signal density

Accuracy anticipated

to improve with increased probe density and better processing

April 30, 2015

Likely

to have usable probe data

Possibly

usable probe data

Likely not usable probe

data

<= 1 signals per mile

AADT > 40000

Fully or Partially captures

>75% slowdowns

<= 2 signals per mile

AADT 20K to 40K

May Fail to capture > 25% of slowdowns

Should be tested

>=2 signals per mile

Not recommendedSlide12

Additional Insights

12

Not ready for Prime Time

Probe data

usable

on highest class arterials

Signal density < 1 per mile on averageTravel times are proportional to ground truthMay still miss some slowdowns, and may want to testConsistent positive bias at low speedsAs probe data improves, delay will increase

Remaining Challenges

Severe

queuing / multi-cycle delays

Optimistic bias with bi-modal traffic

Not sensitive to

signal timing changes

Major disruptions

problematic

April 30, 2015Slide13

Roadmap for Arterial Management SystemsSlide14

Technologies Enabling Arterial Management Systems

Re-identification

Samples vehicle travel time (5% for BT)

Works best at corridor level

Independent of Signal

System

Provides top-level user experience information

High-Res Signal Data

Logs

all

actuation and phasing information

Works best at intersection level

Integrated with Signal System

Provides detailed intersection analysis

Both enabled by consumer wireless communication and big data processing.

Available Now – Multiple Vendors - Cost Effective

Not one or the other… but both!Slide15

Emerging Arterial Performance Measures

Travel Time and Travel Time Reliability – based on sampled travel time sources

Initially re-identification data, later outsourced probe data as it matures, as well as connected vehicle data

Percent Arrivals on Green

Supported by methods such as Purdue Coordination Diagram tools

Split Failures (occurrences)Related to GOR / ROR and similar analysisSlide16

Re-Identification Data (Bluetooth)

Uses a ID unique to a vehicle (MAC ID of a Bluetooth device inside vehicle)

An initial detector identifies when a vehicle enters a corridor by the vehicle’s ID

Another detector

re-identifies

the vehicle at the end of the corridor

Travel time/ speed can be directly calculated from the entry and exit time16

Picture source: libelium.com

Direct samples of Travel TimeSlide17

Re-identification Data FidelitySlide18

Statistical Performance MeasuresSlide19

High Resolution Signal Data

Logging of sensor and phase information

Data forwarded periodically to central server

Applications

Purdue Coordination Diagram

Red-Occupancy Ration / Green Occupancy RatioVolume / Demand Analysis (per movement)

Streamlined MaintenancePicture Source: FHWASlide20

Sample Metric - Intersection

Purdue

Coordination DiagramSlide21

Sample Metric - Intersection

Movement Capacity Analysis (ROR – GOR)Slide22

Current State of Arterial Management Systems (AMS)Slide23

Benefits

Created a language between traffic

engineers, planners, operations,

and management to establish goals, measure performance, and manage the system

Link performance to budget/funding

Systematic approachLong term performance tracking

Better utilization of professional staffOrganizational maturitySlide24

Real-Time Arterial Performance

Stuff from Chapter 1Slide25

Travel-Time Visualization

Picture of Cascading CFD

Picture of ROR and GORSlide26

Intersection Impact – ROR & GORSlide27

Thank You!

Stanley E. Young, P.E. Ph.D.

University of Maryland

Center for Advanced Transportation Technology

5000 College Ave, Bldg 806, #2203

College Park, 20742Mobile  301-792-8180

seyoung@umd.edu